Dash 2020

Row

Suchen in Primo
(im Vergleich zum Vorjahr)

+16 %

Besuche der Website
(im Vergleich zum Vorjahr)

+28 %

Ausleihe von Büchern
(im Vergleich zum Vorjahr)

-35 %

Row

Primo

Column

Primo A

Column

Primo B

Suchen in Primo
Year Search_Total Percent_Change
2018 164620 NA
2019 203863 0.2383854
2020 236113 0.1581945

Website

Column

Chart A

Chart B

Column

Chart C

Chart D

---
title: "Nutzungsstatistiken"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      options(scipen = 999))

library(flexdashboard)
library(readxl)
library(tidyverse)
library(lubridate)
library(gt)
library(janitor)
library(scales)
library(DT)
library(plotly)

# Ressources:
# https://towardsdatascience.com/building-an-hr-dashboard-in-r-using-flexdashboard-76d14ed3f32
# https://rmarkdown.rstudio.com/flexdashboard/using.html

```

Dash 2020 {data-icon="fa-globe"}
===================================== 

Row {data-width=150, data-height=200}
-----------------------------------------------------------------------

### Suchen in Primo 
(im Vergleich zum Vorjahr) ```{r} articles <- "+16 %" valueBox(articles, icon = "fa-search-plus", color = "green", href="#primo") ``` ### Besuche der Website
(im Vergleich zum Vorjahr) ```{r} comments <- "+28 %" valueBox(comments, icon = "fa-user-check", color = "green", href="#website") ``` ### Ausleihe von Büchern
(im Vergleich zum Vorjahr) ```{r} spam <- "-35 %" valueBox(spam, icon = "fa-book", color = "red") ``` ```{r eval=FALSE} ### #Die Zahl bezieht sich auf die prozentuale Veränderung zum Vorjahr 2019. ``` Row ----------------------------------------------------------------------- Primo {data-orientation=columns} ===================================== Column {data-width=400, data-height=400} ----------------------------------------------------------------------- ### Primo A ```{r} Primo_Stat_Auswertungen <- read_excel("T:/Statistik/ALMA_ART/Primo_Stat_Auswertungen.xlsx", sheet = "R_1", col_types = c("date", "text", "numeric")) ``` ```{r} Primo <- Primo_Stat_Auswertungen %>% mutate(Year = year(Date))%>% group_by(Year, Action) %>% summarise(Value = sum(Value)) ``` ```{r} Primo_3 <- Primo %>% summarise(Search_Total = sum(Value)) %>% mutate(Percent_Change = (Search_Total/(lag(Search_Total))-1)) ``` ```{r} ggplot(Primo, aes(x = Year, y = Value)) + geom_col(aes(fill = Action), position = "dodge") + theme_classic() + labs( title = "Primo", subtitle = "Anzahl Suchen von 2018 bis 2020", fill = "Suchen", y = "", x = "Jahre" ) + theme(legend.position = "bottom") #+ # facet_wrap(~Date) ``` Column {data-width=200, data-height=200} ----------------------------------------------------------------------- ### Primo B ```{r} Primo_2 <- Primo %>% spread(key = Year, value = Value) %>% adorn_totals("row") ``` ```{r} gt(Primo_3) %>% tab_header( title = md("Suchen in Primo")) ``` Website {data-orientation=columns} ===================================== Column {data-width=400} ----------------------------------------------------------------------- ### Chart A ```{r} Website_Besuche <- read_excel("T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Besuche", col_types = c("numeric", "text", "numeric")) ``` ```{r} Website_Besuche_2 <- Website_Besuche %>% group_by(Action) %>% # filter(Action == "Besuche") %>% mutate(Percent_Change = (Value/(lag(Value))-1)) ``` ```{r} p1 <- ggplot(Website_Besuche, aes(x=Date, y = Value))+ geom_col(aes(fill = Action), position = "dodge")+ theme_classic()+ labs(title = "Besuche und Seitenansichten 2018 bis 2020", fill = "", y = "", x = "") ggplotly(p1) ``` ### Chart B ```{r} Website_OS <- read_excel("T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Desktop", col_types = c("text", "text", "numeric")) ``` ```{r} p1 <- Website_OS %>% filter(date == "2020") %>% plot_ly() %>% add_pie(labels=Website_OS$action, values = Website_OS$value, hole = 0.6) %>% layout(title="Verwendung Betriebssyteme 2020") p1 ``` Column {data-width=400} ----------------------------------------------------------------------- ### Chart C ```{r} Website_Users <- read_excel("T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Users", col_types = c("date", "numeric", "numeric")) ``` ```{r} Website_Users_2 <- Website_Users %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) p2 <- ggplot(Website_Users_2, aes(x = month, y = visits, color = year))+ geom_point(size=2)+ geom_line(aes(x = month, y = visits, group = year), size = 1)+ theme(axis.text.x = element_text(angle = 90))+ # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic()+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ labs(title = "Besuche 2018 bis 2020", y = "Besuche", x = "", color = "") ggplotly(p2) ``` ### Chart D ```{r} Website_Users_2 <- Website_Users %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) #%>% # filter(date >= "2019-01-01") p1 <- ggplot(Website_Users_2, aes(x = month, y = unique_users, color = year))+ geom_point(size=2)+ geom_line(aes(x = month, y = unique_users, group = year), size = 1)+ theme(axis.text.x = element_text(angle = 90))+ # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic()+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ labs(title = "Unique Users 2018 bis 2020", y = "Unique Users", x = "", color = "") ggplotly(p1) ```